In this course, you will explore a variety of open-source technologies for working with geosptial data, performing spatial analysis, and undertaking general data science. The first component of the class focuses on the use of QGIS and associated technologies (GDAL, PROJ, GRASS, SAGA, and Orfeo Toolbox). The second component of the class introduces Python and associated open-source libraries and modules (NumPy, Pandas, Matplotlib, Seaborn, GeoPandas, Rasterio, WhiteboxTools, and Scikit-Learn) used by geospatial scientists and data scientists. We also provide an introduction to Structured Query Language (SQL) for performing table and spatial queries. This course is designed for individuals that have a background in GIS, such as working in the ArcGIS environment, but no prior experience using open-source software and/or coding. You will be asked to work through a series of lecture modules and videos broken into several topic areas, as outlined below. Fourteen assignments and the required data have been provided as hands-on opportunites to work with data and the discussed technologies and methods. If you have any questions or suggestions, feel free to contact us. We hope to continue to update and improve this course. This course was produced by West Virginia View (http://www.wvview.org/) with support from AmericaView (https://americaview.org/). This material is based upon work supported by the U.S. Geological Survey under Grant/Cooperative Agreement No. G18AP00077. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey. After completing this course you will be able to: apply QGIS to visualize, query, and analyze vector and raster spatial data. use available resources to further expand your knowledge of open-source technologies. describe and use a variety of open data formats. code in Python at an intermediate-level. read, summarize, visualize, and analyze data using open Python libraries. create spatial predictive models using Python and associated libraries. use SQL to perform table and spatial queries at an intermediate-level.

This course demonstrates how to select, modify, create, and share web applications using ArcGIS Online. ArcGIS Online offers many different options for creating web applications that share web maps, web scenes, and spatial functions. But how do you decide which web application best meets your requirements? Each web application option implements different functions and showcases a specific look and feel. You can choose a web application that meets your organization's functional requirements, apply your organization's look and feel, and share your web map without writing any code.Two workflows will be introduced for creating web applications using ArcGIS Online:Applying your web map to an existing template applicationCreating your own web application using Web AppBuilder for ArcGISAfter completing this course, you will be able to do the following:Identify the components of a web application.Create a web application from an existing configurable app template.Create a web application using Web AppBuilder for ArcGIS.Use ArcGIS Online to deploy a web application.

EGLE administers the statewide Michigan Green Schools certification program. The program is dedicated to assisting all Michigan schools public and private achieve environmental goals that include protecting the air, land, water and animals of our state along with world outreach through good ecological practices and the teaching of educational stewardship of students pre-kindergarten through high school.A school is eligible to receive a Green School, Emerald School, or Evergreen School Environmental Stewardship Designation if the school or students perform the required number of activities, with a minimum of two activities from each of the four categories. The activity requirements for each level of environmental stewardship designation are as follows:Fields included in this dataset are:SchoolName: The name of the school.SchoolCity: The city that the school is in.SchoolCounty: The county that the school is in.CountyCoordName: The name of the county coordinator that approved the schoolCertificationLevel: The Green Schools certification level achieved based on number of activities achieved.Green: 10 total activities with at least two activities from each of the four categories.Emerald: 15 total activities with at least two activities from each of the four categories.Evergreen: 20 total activities with at least two activities from each of the four categories.Awaiting Final Result: Macomb County has not sent the final certification levels to the State of Michigan.Please visit EGLE's Green School site for more information and direct questions to Sam Lichtenwald, EGLE's Michigan Green Schools Coordinator, at LichtenwaldS@Michigan.gov.

Governor's Island Dataset for GRASS GIS
This geospatial dataset contains raster and vector data for Governor's Island, New York City, USA. The top level directory governors_island_for_grass is a GRASS GIS location for NAD_1983_StatePlane_New_York_Long_Island_FIPS_3104_Feet in US Surveyor's Feet with EPSG code 2263. Inside the location there is the PERMANENT mapset, a license file, data record, readme file, workspace, color table, category rules, and scripts for data processing. This dataset was created for the course GIS for Designers.

Instructions
Install GRASS GIS, unzip this archive, and move the location into your GRASS GIS database
directory. If you are new to GRASS GIS read the first time users guide.

Data Sources

• https://orthos.dhses.ny.gov/
• https://data.cityofnewyork.us/

Maps

• Orthophotographs from 2012, 2014, 2016, 2018, and 2020
• Digital elevation model from 2017
• Digital surface models from 2014 and 2017
• Landcover from 2014

License
This dataset is licensed under the ODC Public Domain Dedication and License 1.0 (PDDL) by Brendan Harmon.

In this course, you will learn to work within the free and open-source R environment with a specific focus on working with and analyzing geospatial data. We will cover a wide variety of data and spatial data analytics topics, and you will learn how to code in R along the way. The Introduction module provides more background info about the course and course set up. This course is designed for someone with some prior GIS knowledge. For example, you should know the basics of working with maps, map projections, and vector and raster data. You should be able to perform common spatial analysis tasks and make map layouts. If you do not have a GIS background, we would recommend checking out the West Virginia View GIScience class. We do not assume that you have any prior experience with R or with coding. So, don't worry if you haven't developed these skill sets yet. That is a major goal in this course. Background material will be provided using code examples, videos, and presentations. We have provided assignments to offer hands-on learning opportunities. Data links for the lecture modules are provided within each module while data for the assignments are linked to the assignment buttons below. Please see the sequencing document for our suggested order in which to work through the material. After completing this course you will be able to: prepare, manipulate, query, and generally work with data in R. perform data summarization, comparisons, and statistical tests. create quality graphs, map layouts, and interactive web maps to visualize data and findings. present your research, methods, results, and code as web pages to foster reproducible research. work with spatial data in R. analyze vector and raster geospatial data to answer a question with a spatial component. make spatial models and predictions using regression and machine learning. code in the R language at an intermediate level.

The Hills of Governor's Island Dataset for GRASS GIS This geospatial dataset contains raster and vector data for the Hills region of Governor's Island, New York City, USA. The top level directory governors_island_hills_for_grass is a GRASS GIS location for NAD_1983_StatePlane_New_York_Long_Island_FIPS_3104_Feet in US Surveyor's Feet with EPSG code 2263. Inside the location there is the PERMANENT mapset, a license file, data record, readme file, workspace, color table, category rules, and scripts for data processing. This dataset was created for the course GIS for Designers.

Instructions Install GRASS GIS, unzip this archive, and move the location into your GRASS GIS database directory. If you are new to GRASS GIS read the first time users guide.

Data Sources

https://orthos.dhses.ny.gov/

https://data.cityofnewyork.us/

Maps

Orthophotographs from 2012, 2014, 2016, 2018, and 2020

Digital elevation model from 2017

Digital surface models from 2014 and 2017

Landcover from 2014

License This dataset is licensed under the ODC Public Domain Dedication and License 1.0 (PDDL) by Brendan Harmon.

The information in the abstract is translated from the archaeological report: Norrköping municipality is planning for new industrial estates and road construction works by Herstadberg, Kvillinge parish, and for the moving and expansion of Ingelsta golf course. A previous archaeological assessment (phase 1) showed that there are several known archaeological sites within and nearby the survey area, as well as several areas which are likely to contain unmarked archaeological remains. A great number of trial trenches were dug during phase 2 of the assessment. Settlement remnants from the 17th or 18th century and what was probably a prehistoric hearth were found. To relate the remains closer to their context and dating, a more detailed analysis of historical maps was performed. The analysis showed that a farm has stood on the site, and that there are several abandoned building sites in the vicinity, deserted already in the 17th century and located on the site of the medieval farm Ströbo. The analysis also showed that the farms of Herstad village were not built on a common village site. Except for the remnants mentioned above no archaeological features or artefacts were found and no further archaeological investigations will thus take place on the site. The Herstad village site has kept the temporary designation UV 3, divided into UV 3a - c for the individual farms within the village. The deserted medieval farm of Ströbo has got the temporary designation UV 4, while waiting for an official ancient monuments record number.

Purpose:

The information in the purpose is translated from the archaeological report: The purpose of the assessment (phase 2) was to determine whether or not the development area contained archaeological remains, and to document and, if possible, date the features and artefacts.

The ZIP file consists of shapefiles and an Access database with information about the excavations, findings and other metadata about the archaeological survey.

This course teaches how to best symbolize your map data so that your audience gets the information that it needs.Goals Apply principles of map symbology to map features. Understand basic principles of map symbology.

ABSTRACT Drawing on the vagueness of what digital humanities are, and the view that sharing concrete experiences enriches that debate, the article offers an account of initiatives undertaken within the History Department of the Federal University of São Paulo (Unifesp). To give greater diversity to the material presented, projects were chosen from a research group and an undergraduate course. With centers of gravity in research and teaching, the projects analyzed propose greater recognition of the student’s role in digital humanities. The analysis also reinforces the paradox that digital humanities projects are institutionally undervalued, while facilitating the collaboration and the free circulation of knowledge.

This layer displays a global map of land use/land cover (LULC) derived from ESA Sentinel-2 imagery at 10m resolution. Each year is generated with Impact Observatory’s deep learning AI land classification model, trained using billions of human-labeled image pixels from the National Geographic Society. The global maps are produced by applying this model to the Sentinel-2 Level-2A image collection on Microsoft’s Planetary Computer, processing over 400,000 Earth observations per year.The algorithm generates LULC predictions for nine classes, described in detail below. The year 2017 has a land cover class assigned for every pixel, but its class is based upon fewer images than the other years. The years 2018-2024 are based upon a more complete set of imagery. For this reason, the year 2017 may have less accurate land cover class assignments than the years 2018-2024. Key Properties Variable mapped: Land use/land cover in 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024Source Data Coordinate System: Universal Transverse Mercator (UTM) WGS84Service Coordinate System: Web Mercator Auxiliary Sphere WGS84 (EPSG:3857)Extent: GlobalSource imagery: Sentinel-2 L2ACell Size: 10-metersType: ThematicAttribution: Esri, Impact ObservatoryAnalysis: Optimized for analysisClass Definitions: ValueNameDescription1WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2TreesAny significant clustering of tall (~15 feet or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields.10CloudsNo land cover information due to persistent cloud cover.11RangelandOpen areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.NOTE: Land use focus does not provide the spatial detail of a land cover map. As such, for the built area classification, yards, parks, and groves will appear as built area rather than trees or rangeland classes.Usage Information and Best PracticesProcessing TemplatesThis layer includes a number of preconfigured processing templates (raster function templates) to provide on-the-fly data rendering and class isolation for visualization and analysis. Each processing template includes labels and descriptions to characterize the intended usage. This may include for visualization, for analysis, or for both visualization and analysis. VisualizationThe default rendering on this layer displays all classes.There are a number of on-the-fly renderings/processing templates designed specifically for data visualization.By default, the most recent year is displayed. To discover and isolate specific years for visualization in Map Viewer, try using the Image Collection Explorer. AnalysisIn order to leverage the optimization for analysis, the capability must be enabled by your ArcGIS organization administrator. More information on enabling this feature can be found in the ‘Regional data hosting’ section of this help doc.Optimized for analysis means this layer does not have size constraints for analysis and it is recommended for multisource analysis with other layers optimized for analysis. See this group for a complete list of imagery layers optimized for analysis.Prior to running analysis, users should always provide some form of data selection with either a layer filter (e.g. for a specific date range, cloud cover percent, mission, etc.) or by selecting specific images. To discover and isolate specific images for analysis in Map Viewer, try using the Image Collection Explorer.Zonal Statistics is a common tool used for understanding the composition of a specified area by reporting the total estimates for each of the classes. GeneralIf you are new to Sentinel-2 LULC, the Sentinel-2 Land Cover Explorer provides a good introductory user experience for working with this imagery layer. For more information, see this Quick Start Guide.Global land use/land cover maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land use/land cover anywhere on Earth. Classification ProcessThese maps include Version 003 of the global Sentinel-2 land use/land cover data product. It is produced by a deep learning model trained using over five billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world.The underlying deep learning model uses 6-bands of Sentinel-2 L2A surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map for each year.The input Sentinel-2 L2A data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch. CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.

Our Co-design team is from the University of Texas, working on a Department of Energy-funded project focused on the Beaumont-Port Arthur area. As part of this project, we will be developing climate-resilient design solutions for areas of the region. More on www.caee.utexas.edu. We captured aerial photos in the Port Arthur Coastal Neighborhood Community and the Golf Course on Pleasure Island, Texas, in June 2024. Aerial photos taken were through DroneDeploy autonomous flight, and models were processed through the DroneDeploy engine as well. All aerial photos are in .JPG format and contained in zipped files for each area. The processed data package includes 3D models, geospatial data, mappings, and point clouds. Please be aware that DTM, Elevation toolbox, Point cloud, and Orthomosaic use EPSG: 6588. And 3D Model uses EPSG: 3857. For using these data: - The Adobe Suite gives you great software to open .Tif files. - You can use LASUtility (Windows), ESRI ArcGIS Pro (Windows), or Blaze3D (Windows, Linux) to open a LAS file and view the data it contains. - Open an .OBJ file with a large number of free and commercial applications. Some examples include Microsoft 3D Builder, Apple Preview, Blender, and Autodesk. - You may use ArcGIS, Merkaartor, Blender (with the Google Earth Importer plug-in), Global Mapper, and Marble to open .KML files. - The .tfw world file is a text file used to georeference the GeoTIFF raster images, like the orthomosaic and the DSM. You need suitable software like ArcView to open a .TFW file. This dataset provides researchers with sufficient geometric data and the status quo of the land surface at the locations mentioned above. This dataset could streamline researchers' decision-making processes and enhance the design as well.

Abstract: Quality of life information is one of the factors taken into consideration when a business is deciding where to expand or relocate. The Virginia Economic Development Partnership has developed a golf course data layer to use while assisting businesses during this decision making process. The list Citation_Information: Originator: Virginia Economic Development Partnership Publication_Date: 20021001 Title: Golf Courses in Virginia Geospatial_Data_Presentation_Form: vector digital data Other_Citation_Details: None Online_Linkage: Online_Linkage: of golf courses originally came from the 2001 Virginia Golf Course directory published by the Virginia Tourism Corporation in association with the Virginia State Golf Association (VSGA). Since that time, the list of courses has been updated using new directories as well as various websites including , , , and . The final product includes boundaries (polygons) and points for public, private, semi-private, resort, and military golf courses in Virginia. The boundaries and points were originally located by geocoding street addresses and verifying and correcting the result using USGS DOQs (1994-1998) and SPOT satellite imagery (2001). Other sources for boundaries included the Virginia Atlas and Gazetteer and ADC street map books. The golf course locations were corrected in 2003 to match the Virginia Base Mapping Program imagery (VBMP), and all updates since that time have been based on the VBMP as well. Purpose: The purpose of this data is to provide a geographic representation of the location of each golf course for tourism and economic development. This data is also used in landuse classification projects. Accessed through: http://gis.vedp.org/data_search.aspx Access to metadata: http://gis.vedp.org/meta/golf_meta.htm Accessed On January 24, 2008.

Active non-wholesale non-transmission Electric Utility Service Areas as listed in the Regulatory Commission of Alaska (RCA) Certificate details for regulated utilities. Likely the most comprehensive collection of State of Alaska utility service areas - but not necessarily definitive for every utility. For complicated large city service areas such as water and sewer the GIS department that represents those cities might have the best representation of the service area. There are also utilities that may not be regulated by RCA which will not be in the data. Footprints in general were lifted from existing KML files created by a contractor in the years 2008-2017. Service area changes that have happened since 2008 may not yet be reflected in the footprints. In a few cases legal descriptions had typos which resulted in service areas miles from the community they intended to cover. In the case of the AsOfDate attribute in this dataset only reflects the date of the last syncing of master certificate metadata with RCA Library database - not the current polygon representation.Source: Regulatory Commission of AlaskaThis data has been visualized in a Geographic Information Systems (GIS) format and is provided as a service in the DCRA Information Portal by the Alaska Department of Commerce, Community, and Economic Development Division of Community and Regional Affairs (SOA DCCED DCRA), Research and Analysis section. SOA DCCED DCRA Research and Analysis is not the authoritative source for this data. For more information and for questions about this data, see: Regulatory Commission of Alaska Library

In the course of the extensive survey of Sikyonia (1996-2002) by Yannis Lolos, more than 250 archaeological sites were recorded and mapped, including settlements, forts and watch/signal towers, roads, sanctuaries, quarries, aqueducts and cisterns, terracing walls, and other types of remains.

Out of the 250 archaeological sites that we have mapped and examined in Sikyonia, we identified 148 as representing areas of habitation, with the earliest going back to the Neolithic period and the latest to the 19th century. We observed a certain concentration of prehistoric sites in proximity to the coastal plain (10 out of the 18 sites of this period), where also lies the most important prehistoric settlement site of Sikyonia that we have recorded, namely Litharakia of Krines, with a ceramic surface scatter of ca. 3 ha and an occupation from the Neolithic to the Geometric period. Habitation in Sikyonia followed a rising course from the 6th millennium to the Late Helladic period, and from the Geometric to the Classical period, where it reached its peak.

The Classical period saw the appearance of the largest settlements outside the city (in 12 of those we observed a surface material scatter of over 2 ha), but also a multitude of smaller settlements (scattered over an area between 0.1 and 0.8 ha) which probably represent isolated farmsteads. For the satisfaction of the vital needs of the population, people have now started cultivating even marginal areas, semi-mountainous and usually lying on a slope, and attempted to improve their fertility by constructing retaining walls and other infrastructure works. In later Hellenistic, and less so in Roman times, we witness a shrinkage of settlement sites in the chora of Sikyonia. Recovery will come in Late Roman times, a phenomenon witnessed also in other areas of the Greek world. During this period we observed a tendency for medium and large sites with a corresponding reduction in the number of smaller sites, which suggests a growing preference for communal living. In addition, churches and monasteries now appear in the countryside, a tendency continuing during the Byzantine and post-Byzantine period, with the best example being that of the monastery of Lechova on mount Vesiza.

The reduction of the rural settlement sites in Hellenistic and Roman times may be due to a general demographic crisis or (and) to the concentration of the population in the refounded city – the plateau of Vasiliko. The location and mapping of the visible segments of the ancient walls of Sikyon showed that the entire plateau, ca. 230 ha of surface, was intramuros.

Data

The data in this collection is spatial data collected and created from the project. It is all in GML format which can be used in most GIS applications.

This style for ArcGIS Pro contains four north arrows. They have a glassy semitransparent white appearance with a shadow effect for better visibility over highly textured surfaces while muted enough to provide balance.Before you use a north arrow, though, ask yourself if your map really needs one.Plus they're a bit of fun sizzle. Will they look good over your map? Maybe! I wouldn't try them over a solid basemap though. They will look pretty bad probably. They are intended for the busy high contrast varied hues of an imagery basemap. But of course you will do what you feel is right, which may include not using them for any map.There is an arrowhead style north arrow and a cardinal ring arrow. These are standard north arrow shapes available in ArcGIS Pro, but given the glassy appearance. A stylized "N" and a minimalist arrow were drawn as custom SVGs then added to ArcGIS Pro and given the glassy appearance.Enjoy! John Nelson

Active non-wholesale Sewer and Provisional Sewer Utility Service Areas as listed in the Regulatory Commission of Alaska (RCA) Certificate details for regulated utilities. Likely the most comprehensive collection of State of Alaska utility service areas - but not necessarily definitive for every utility. For complicated large city service areas such as water and sewer the GIS department that represents those cities might have the best representation of the service area. There are also utilities that may not be regulated by RCA which will not be in the data. Footprints in general were lifted from existing KML files created by a contractor in the years 2008-2017. Service area changes that have happened since 2008 may not yet be reflected in the footprints. In a few cases legal descriptions had typos which resulted in service areas miles from the community they intended to cover. In the case of the AsOfDate attribute in this dataset only reflects the date of the last syncing of master certificate metadata with RCA Library database - not the current polygon representation.Source: Regulatory Commission of AlaskaThis data has been visualized in a Geographic Information Systems (GIS) format and is provided as a service in the DCRA Information Portal by the Alaska Department of Commerce, Community, and Economic Development Division of Community and Regional Affairs (SOA DCCED DCRA), Research and Analysis section. SOA DCCED DCRA Research and Analysis is not the authoritative source for this data. For more information and for questions about this data, see: Regulatory Commission of Alaska Library

Update date: from GISP repository on 2/6/25. This is a static dataset.Data Type: polyline dataAn open channel is digitized from paper or scanned imagery.Subtypes:Improved: An open drainage course confined with lined or unlined embankments.Unimproved: A natural drainage course graded to channelize storm water.Swale: A graded depression with relatively low slope to channelize storm water. Ditch: A trench provided to channelize storm water. Attributes: Most of the feature classes in this storm drain geometric network share the same GIS table schema. Only the most critical attributes per operations of the Los Angeles County Flood Control District are listed below:AttributeDescriptionASBDATEThe date the design plans were approved "as-built" or accepted as "final records".CROSS_SECTION_SHAPEThe cross-sectional shape of the pipe or channel. Examples include round, square, trapezoidal, arch, etc.DIAMETER_HEIGHTThe diameter of a round pipe or the height of an underground box or open channel.DWGNODrain Plan Drawing Number per LACFCD NomenclatureEQNUMAsset No. assigned by the Department of Public Works (in Maximo Database).MAINTAINED_BYIdentifies, to the best of LAFCD's knowledge, the agency responsible for maintaining the structure.MOD_DATEDate the GIS features were last modified.NAMEName of the individual drainage infrastructure.OWNERAgency that owns the drainage infrastructure in question.Q_DESIGNThe peak storm water runoff used for the design of the drainage infrastructure.SOFT_BOTTOMFor open channels, indicates whether the channel invert is in its natural state (not lined).SUBTYPEMost feature classes in this drainage geometric nature contain multiple subtypes. 1 = Improved, 2 = Unimproved, 3 = Ditch, 4 = SwaleUPDATED_BYThe person who last updated the GIS feature.WIDTHWidth of a channel in feet.This Storm Drain Dataset is a work in progress, and all users of this data are STRONGLY ENCOURAGED to obtain the most current copy, available for download at the LA County eGIS Hub site.Terms of UseThis data is derived from the County Cadastral Landbase and features are typically added to this dataset per recorded 'as-built' drawings. Accurate facility locations on the ground must be determined by qualified field personnel. If any errors are found, or if there are general questions, please contact the individuals listed in the Credits.This product is for information purposes and should not be used for legal, engineering, or survey purposes. County assumes no liability for any errors or omissions.

This dataset provides a land cover map focused on peatland ecosystems in the upper peninsula of Michigan. The map was produced at 12.5-m resolution using a multi-sensor fusion (optical and L-band SAR) approach with imagery from Landsat-5 TM and ALOS PALSAR collected between 2007 and 2011. A random forest classifier trained with polygons delineated from field data and aerial photography was used to determine pixel classes. Accuracy assessment based on field-sampled sites show high overall map accuracy (92%).

Public Safety Power Shutoffs (PSPS) could impact customers in highest fire-risk communities in Arizona, including parts of Coconino, Gila, Navajo, Pinal, and Yavapai counties. This Public Safety Power Shutoff (PSPS) map is designed to provide customers with a visual display of the general areas where APS may implement a PSPS. Customers can use this map to search addresses to determine if an area is considered PSPS.Data was pieced together by various business units at APS, including GIS, Customer Billing, and Distribution Operations. Many business units and departments came together to discuss and approve this public facing map. Several departments include Customer Experience and Solutions as well as Public Affairs. The GIS department assisted and supported this effort over the course of 6 months.

Eelgrass Beds Historic Set:

Historic Eelgrass Points is a 1:24,000-scale, point feature-based layer that depicts the locations of historic eelgrass beds (Zostera marina) in Long Island Sound, the Connecticut River, the Quinnipiac River and other bays, harbors and waterbodies in Connecticut's coastal area. It also includes several points located along the north shore of Long Island. There are a total of 131 point features, the majority of which are located east of the Connecticut River. Point features in this layer are compiled from two major sources: 1) the polygon feature label points in the Historic Eelgrass Beds polygon layer representing sources with a mapping component; and 2) additional points that were based on historic literature review that had no mapping component. Source information including source description and collection date for each point is described in the layer's table data. Feature locations are inexact. Because of the variety of source maps and methods used for their automation, this coverage should be considered to have limited spatial accuracy and is appropriate for general uses only. Actual data collection ranged from 1873 through 1996. This layer was published in 1997 and is not updated. It does not represent current conditions.

Historic Eelgrass Bed Polygons is a 1:24,000-scale, polygon feature-based layer that depicts the locations of historic eelgrass beds (Zostera marina) in Long Island Sound and the Niantic River, as well as in other bays, harbors and waterbodies in Connecticut's coastal area. It also includes several points located along the north shore of Long Island. There are a total of 52 polygon features, all of which (except the Long Island points), are located within or east of the Niantic River. This layer can be used with Historic Eelgrass Points. This layer does not represent current conditions. Rather, it depicts historic eelgrass bed locations that were observed and defined either cartographically or narratively over the course of many years and from various sources. The dates of each source's data collection are noted in the attribute table. Feature locations are inexact. Because of the variety of source maps and methods used for their automation, this information should be considered to have limited spatial accuracy and is appropriate for general uses only. The data was taken from maps of various scales and projections that were drawn between 1905 and 1996. These maps were reduced to approximately 1:24,000 scale and adjusted for best fit; eelgrass areas were redrafted onto USGS Topographic Quadrangle maps for digitizing. In order to create a single polygon coverage, areas were considered to represent a maximum extent of eelgrass beds. This layer was published in 1997 and is not updated.